SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses.
View Article and Find Full Text PDFDue to the complex nature of common diseases, their etiology is likely to involve "uncommon but strong" (UBS) interactive effects--i.e. allelic combinations that are each present in only a small fraction of the patients but associated with high disease risk.
View Article and Find Full Text PDFAberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20-50 nodes).
View Article and Find Full Text PDFHuman embryonic stem cell (hESC) lines are derived from the inner cell mass (ICM) of preimplantation human blastocysts obtained on days 5-6 following fertilization. Based on their derivation, they were once thought to be the equivalent of the ICM. Recently, however, studies in mice reported the derivation of mouse embryonic stem cell lines from the epiblast; these epiblast lines bear significant resemblance to human embryonic stem cell lines in terms of culture, differentiation potential and gene expression.
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